The presentation includes the following sections:

Introduction
Previous research
Materials and Method
Climate scenario and models
Crop simulation model
Calibration and validation
Future projections of some climatic parameters
Cotton yield response to climate change
Assessment of the used climatic models
Comparison 2071-2100 and 1961-1990
Conclusions
Acknowledgement

Economou, Garifalia

Kapsomenakis, John

Karamanos, Andreas

Kotoulas, Vasilios

Vahamidis, Petros

Voloudakis, Dimitrios

Zerefos, Christos

Digital Repository of Hellenic Managing Authority of the Operational Programme &quot;Education and Lifelong Learning&quot; (EDULLL)

Στυλ κύριου υπότιτλουPREDICTION OF CLIMATE CHANGE IMPACTS ON COTTON YIELDS IN GREECE UNDER EIGHT CLIMATIC MODELS USING THE AQUACROP CROP SIMULATION MODEL.Dimitrios Voloudakis(1), Andreas Karamanos(1), Garifalia  Economou(1), Petros Vahamidis(1), Vasilios Kotoulas(1), John Kapsomenakis(2), Christos Zerefos(2)(1) Agricultural University of Athens, Laboratory of Agronomy, 75 Iera Odos str, 11855 Athens , Greece (2) Research Centre for Atmospheric Physics and Climatology, Academy of Athens, 84 Solonos str, 10680 Athens, Greece1st CIGR Inter-Regional Conference on Land and Water Challenges, Bari 10-14 September 2013Introduction▪ Why Greece?▪ IPCC 4th report  Southern Europe = vulnerable to climate change▪ Why cotton?▪ Greece is the largest producer in EU ( 80% total production)▪ Greek Cotton added value is 426 million €▪ Goal: integrated view of the future cotton yieldPrevious research▪ PESETA project (2009)    Mediterranean South Europe +5% to -27%▪ Giannakopoulos  et al (2009)             C3 crops -8% to +5% until 2060For cotton in  Greece▪ Greek Ministry of Environment (1997)     -29% Thessaly&Macedonia    +21% Thrace▪ Greek Climate Change Study Committee (2011)increase in Eastern Macedonia- Thrace and Western Macedonia decline in Central Eastern Greece          Materials and Method▪ Study AreasAlexandroupoli –Eastern Macedonia/Thrace (NE)Mikra- Western and Central Macedonia (N-NW)Arta&Agrinio – Western Greece (W)Karditsa&Yliki- Eastern and Central Greece (C-E)Pyrgos- Western Peloponnese (SW) AGRINIO ARTA YLIKI ALEX/LI PYRGOS MIKRA KARDITSAmean1,87 1,96 2,00 1,41 2,24 2,12 2,21Standard error ±0,06 ±0,05 ±0,06 ±0,06 ±0,06 ±0,04 ±0,06Seedcotton yields (tn/h) in the seven study areas for the period 1961-1990Climate scenario and models§ Emission Scenario A1B IPCC (2001)§ CO2  359ppm (1990) 532ppm(2050) 714ppm(2100)§ Temp +2˚C (2050) +4˚C (2100)  § Precipitation -10% (2050) -25% (2100)§ Climate Models derived from the ENSEMBLES project§ HadRM3§ C4I§ REMO-MPI§ ETHZ§ CNRM§ DMI-HIRHAM§ KNMI§ SMHI§ Max&min Temperature,  Relative humidity, Wind speed, Solar irradiance, PrecipitationCrop simulation model§ AquaCrop is a crop water productivity model developed by the Land and Water Division of the Food and Agricultural Organisation (FAO)Basic Components§  the climate, with its thermal regime, rainfall, evaporative demand and carbon dioxide       concentration;§  the crop, with its development, growth and yield processes;§  the soil, with its water (and salt) balance;§  the management, with practices including irrigation, fertilization and mulching.§ Simulation of cotton yield with the use of AquaCrop      Farahani et al, 2009; Garcia-Vila et al, 2009; Hussein et al, 2011 etc.§ AquaCrop simulations respond to changes in CO2 concentration (Vanuytrecht et al., 2011)▪ AtmosphereDaily minimum-maximum air temperature, rainfall, ETo, CO2 annual concentration§ CropPhenology, canopy cover, rooting depth, biomass production, harvestable yield, reduction of the canopy expansion rate, acceleration of senescence, closure of stomata, planting/sowing data, thermal based on Growing Degree Days (GDD)§ Soilhydraulic conductivity at saturation, volumetric water content at saturation, field capacity, wilting point§ Field Management§ Irrigation managementType: drip irrigation, Fixed time    Years  Crop characteristics 2005 2006 2007Yield (tn/ha)  Observed 4.05 3.65 2.97Simulated 4.02 3.67 3.26         RMSE   0.17          d   0.94  Biomass (tn/ha)  Observed 14.09 12.85 12.10Simulated 14.25 12.86 11.20         RMSE   0.49          d   0.93  Filed experiment Karditsa from 2005 to 2007Calibration and validationFuture projections of some climatic parametersCotton yield response to climate changeAgrinio Alex/lis Arta Karditsa Mikra Pyrgos Yliki 1961-19901961-19901961-19901961-19901961-19901961-19901961-19901961-19901961-19901961-19901961-19901961-19901961-19901961-19902021-20502071-21002021-20502071-21002021-20502071-21002021-20502071-21002021-20502071-21002021-20502071-21002021-20502071-2100HadRM3 -3.48 -8.71 -137.81 -109.41 12.38 36.49 47.19 19.99 -30.14 -69.66 -24.03 -63.75 -31.35 -102.88C4I -0.54 22.56 27.26 -23.84 16.8 7.04 3.38 -11.09 8.01 -18.02 7.69 25.52 20.28 9.57REMO-MPI na na 43.75 43.1 45.84 51.19 7.45 31.16 11.63 38.17 25.33 30.75 8.39 32.2ETHZ 17.14 20.07 72.2 70.39 12.27 18.67 14.86 25.49 36.04 47.9 7.89 17.87 19.37 27.9CNRM -21.54 -3.85 76.94 51.51 na na -71.27 -8.53 -10.67 -3.51 -0.73 -37.78 49.31 43.28DMI-HIRHAM19.22 28.49 -15.5 -5.06 24.08 31.59 -2.75 28.37 -11.56 7.1 4.48 10.18 7.82 19.81KNMI 8.83 13.58 na na 6.61 19.77 16.34 24.94 4.4 -4.57 13.32 19.28 21.27 36.58SMHI na na na na na na -14.79 46.93 na na na na 35.73 40.5Assessment of the used climatic models▪ Discriminant Analysis is a tool to find a linear combination of features which separates two or more classes of objects or events (case).▪ Discriminant functionsD= v1 X1 + v2X2 + v3X3 + …..+ viXi+aD=discriminant function, v=discriminant coefficient, X=respontent’s score for that variable, a=constant , i=number of predictorsThe 1st function maximizes the differences between groups on that function (higher value of v)§ the standardized discriminant coefficients are used in assessing the contribution to the discriminant function of each independent variable.§ In the end DF tries to correctly classify the higher percentage of the grouped casesStandardized Discriminant function coefficients for the three different periods of the functions 1 and 2.Models 1961-1990   2021-2050   2071-2100Standardized Standardized Standardizedfunc 1 func 2 func 1 func 2 func 1 func 2HadCM3C4I 0.569 -0.504 0.556 0.01 0.648 0.018REMO-MPI 0.328 0.608 0.383 0.821 -0.258 0.38CNRM 0.176 0.527 -0.36 0.344 0.223 0.221DMI-HIRHAM 0.454 -0.118 0.456 0.105 0.474 -0.162KNMI 0.397 0.185 0.383 -0.535 0.483 -0.584SMHI 0.598 -0.24 0.187 0.869ΕΤΗΖ 0.526 0.161            1961-1990 Function 1 explained 65.6% of the simulated cotton yields variance2021-2020 Function 1 explained 59.7% of the simulated cotton yields variance2071-2100 Function 1 explained 53.0% of the simulated cotton yields variancePlot of the first two discriminant functions for the simulated cotton yield under 7 different areas during the period 1961-1990. 1: Agrinio, 2: Alexandroupoli, 3: Arta, 4: Karditsa, 5: Mikra, 6: Pyrgos, 7: Yliki.Plot of the first two discriminant functions for the simulated cotton yield under 7 different areas during the period 2021-2050. 1: Agrinio, 2: Alexandroupoli, 3: Arta, 4: Karditsa, 5: Mikra, 6: Pyrgos, 7: Yliki.Plot of the first two discriminant functions for the simulated cotton yield under 7 different areas during the period 2071-2099. 1: Agrinio, 2: Alexandroupoli, 3: Arta, 4: Karditsa, 5: Mikra, 6: Pyrgos, 7: Yliki.Standardized Discriminant function coefficients for the three different periods of the functions 1 and 2.Models 1961-1990   2021-2050   2071-2100Standardized Standardized Standardizedfunc 1 func 2 func 1 func 2 func 1 func 2HadCM3C4I 0.569 -0.504 0.556 0.01 0.648 0.018REMO-MPI 0.328 0.608 0.383 0.821 -0.258 0.38CNRM 0.176 0.527 -0.36 0.344 0.223 0.221DMI-HIRHAM 0.454 -0.118 0.456 0.105 0.474 -0.162KNMI 0.397 0.185 0.383 -0.535 0.483 -0.584SMHI 0.598 -0.24 0.187 0.869ΕΤΗΖ 0.526 0.161            1961-1990 Function 1 explained 65.6% of the simulated cotton yields variance2021-2020 Function 1 explained 59.7% of the simulated cotton yields variance2071-2100 Function 1 explained 53.0% of the simulated cotton yields varianceComparison of seedcotton maximum and minimum yield change in the seven study areas during 2021-2050 as percentage of the reference period 1961-1990 according to the climate models C4I and DMIComparison 2021-2050 and 1961-1990▪ Arta, Pyrgos and Agrinio (Western Greece) and Yliki (Central Greece) will be more favoured ▪ Arta (increases from 16.8 to 24.08%)▪ Agrinio (-0.54 to 20%)▪ Uncertainty highest in Alexandroupolis (-15.5 to 27.3%) Lower in Karditsa and Mikra (-2.75 to 3.4% and -11.56 to 8% respectively) Comparison of seedcotton maximum and minimum yield change in the seven study areas during 2071-2100 as percentage of the reference period 1961-1990 according to the climate models C4I and DMIComparison 2071-2100 and 1961-1990▪ Arta, Agrinio and Pyrgos will also be more favoured during 2071-2100 (predicted increases 7.04 to 31.59%, 22.56 to 28.5%, and 6 to 19.3% respectively)▪ The most negative impacts of climate change on seedcotton yields will be observed in Alexandroupolis    (-5 to -23.8%)▪ Mikra, Karditsa, and Yliki exhibited a high range of yield fluctuations (-28.3 to 16.7%, -15 to 30.3% and -13.5 to 13% respectively)Conclusions▪ The models C4I and DMI were considered as most reliable for discriminating cotton yields during 1961-1990, 2021-2050 and 2071-2100▪ Positive impacts of climate change on seedcotton yields in the areas of Western Greece (Agrinio, Arta, Pyrgos), and negative impacts or great fluctuations in the other areas (Northern and Central Greece).▪ The magnitude of the changes in the two periods (2021-2050 and 2071-2100) in respect to the reference period of 1961-1990 did not show a definite trend, but changed in the different areas of study▪ Uncertainties: need more reliable crop data from more areas for calibration and validation of AquaCrop ACKNOWLEDGEMENTS▪ This research has been co-financed by the European Union (European Social Fund –ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) – Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund. THANK YOU FOR YOUR ATTENTION

Prediction of climate change impacts on cotton yields in greece under eight climatic models using the aquacrop crop simulation model [PRESENTATION]

The impact of climate change on cotton yields in seven main arable crop sites in Greece (Agrinio, Alexandroupolis, Arta, Karditsa, Mikra, Pyrgos, Yliki) was investigated. The FAO AquaCrop (v.4) water driven model was used as a crop development simulation tool under eight climatic models (HadRM3, C4I, REMO MPI, ETHZ, CNRM, DMI-HIRHAM, KNMI, SMHI) based on IPPC’s A1B Climate Change scenario. The mean values of the models ensemble for temperature and precipitation were +1,8˚C until 2050 and +4 ˚C until the end of the century. The respective values for precipitation were -11% and -24%. The research was applied over three periods, 1961-1990, 2021-2050 and 2071-2099. AquaCrop validation for yield, biomass and canopy cover in respect to field data obtained from experiments carried out in Karditsa (Central Greece) from 2005 to 2007 was satisfactory on the account of Root Mean Square Error (0.17 to 0.49) and Index of Agreement (0.93 to 0.94). AquaCrop model was run using the Growing Degree Day mode in order to account better for the temperature variations. However, it gave erratic results for some specific climatic models (SMHI, KNMI, CNRM) in some years within the period 1961-1990. The predicted yields were highest in locations of western Greece (Agrinio, Arta, Pyrgos), whereas north-eastern Greece (Alexandroupolis) appeared to be less favoured by climate change. A tendency towards increasing yields by the end of the century was detected for the majority of the models. The efficiency of the eight models for yield predictions in the seven sites was assessed by means of a discriminant function analysis. On the account of their function coefficients over the seven sites, it was found that the models DMI and C4I explained consistently a great proportion of variation among the three time periods whereas the models ETHZ, SMHI and KNMI were more efficient in the periods 1961-1990, 2021-2050 and 2071-2099 respectively

Prediction of climate change impacts on cotton yields in greece under eight climatic models using the aquacrop crop simulation model.

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Prediction of climate change impacts on cotton yields in greece under eight climatic models using the aquacrop crop simulation model [PRESENTATION]

Abstract

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Digital Repository of Hellenic Managing Authority of the Operational Programme "Education and Lifelong Learning" (EDULLL)

Digital Repository of Hellenic Managing Authority of the Operational Programme "Education and Lifelong Learning" (EDULLL)

Prediction of climate change impacts on cotton yields in greece under eight climatic models using the aquacrop crop simulation model [PRESENTATION]

Abstract

Similar works

Full text

Available Versions

Digital Repository of Hellenic Managing Authority of the Operational Programme &quot;Education and Lifelong Learning&quot; (EDULLL)

Digital Repository of Hellenic Managing Authority of the Operational Programme &quot;Education and Lifelong Learning&quot; (EDULLL)

Digital Repository of Hellenic Managing Authority of the Operational Programme "Education and Lifelong Learning" (EDULLL)

Digital Repository of Hellenic Managing Authority of the Operational Programme "Education and Lifelong Learning" (EDULLL)